The Data Skills Pathway is a 15-week, hands-on experience where you learn how to turn real-world data into real-world decisions. You’ll build practical skills using raw datasets from instruments and models. The pathway is designed to build skills aligned with roles such as data analyst, meteorologist, operations analyst, and environmental/aviation data technician.

Media: UND Today press release: UND launches Data Skills Pathway to prepare students for AI-age careers

• You work with real data (not textbook-perfect data). You learn how to check data quality, document your process, and make defensible conclusions.
• You learn the full workflow end-to-end. From organizing files and logging metadata → to analysis → to clear communication.
• You connect to live systems and real events. You’ll interpret measurements and patterns tied to actual conditions (not made-up examples).
• You produce a portfolio-ready mini-project. You finish the semester with deliverables you can show to an employer or internship mentor.
• You can bridge into paid research + internships. Strong performance can lead to paid research work and future opportunities.

The Data Skills Pathway is organized into four stages. These stages help students move from an initial interest to a real data-based project, while building research, technical, and communication skills.

You will begin by thinking about topics that interest you. These may include aerospace, weather, aviation, sensors, engineering, cybersecurity, Artificial Intelligence, data science, environmental science, or another area.

During this stage, you will:

• Learn about the Data Skills Pathway.
• Review available project areas.
• Meet instructors, mentors, and other students.
• Complete initial orientation steps.
• Share your interests and goals.
• Begin thinking about what kind of problem you may want to explore.

Goal of this stage: Identify a topic or general area that interests you.

After identifying your interest area, you will work with a mentor to turn that interest into a focused project question.

For example:

• “I am interested in aerospace” may become “How can weather data support safer UAS operations?”
• “I am interested in sensors” may become “How can sensor data be used to detect changes in visibility?”
• “I am interested in cybersecurity” may become “How can data patterns help identify unusual system activity?”

During this stage, you will:

• Discuss project ideas with mentors.
• Choose or refine a project topic.
• Identify possible data sources.
• Prepare a short project brief.
• Define what you want to produce by the end of the pathway.

Goal of this stage: Turn your interest into a clear project question that can be explored using data.

Once your project is defined, you will begin working with data and learning the tools needed for your project.

Depending on your project, you may learn how to:

• Organize files and folders.
• Work with Excel, CSV files, Python, or Jupyter notebooks.
• Clean and check data.
• Create figures, plots, maps, or tables.
• Read and summarize research papers.
• Use basic Artificial Intelligence or machine learning concepts.
• Work with sensor, weather, aviation, engineering, or cybersecurity datasets.
• Document your steps and results.

During Thursday meetings, you will also hear short tool sessions from mentors and share updates on your progress.

Goal of this stage: Learn practical data skills while making progress on your own project.

At the end of the pathway, you will prepare a short final project update. This does not need to be a finished publication-level project. The goal is to clearly explain what you worked on, what data or tools you used, what you found, and what the next step could be.

Your final product may include:

• A figure or table.
• A short script or notebook.
• A cleaned dataset or data summary.
• A short written report.
• A poster or presentation.
• A project reflection.
• A next-step plan for continuing the work.

Goal of this stage: Communicate your work clearly and leave with something useful for future opportunities.

By the end of the Data Skills Pathway, students will have practiced how to turn an interest into a small data-based project. They will work with a mentor, explore a real problem, use data to make progress, and communicate what they learned.

Students will be able to:

• Identify a topic or question that interests them.
• Work with a mentor to define a realistic data-based problem.
• Find, organize, and begin working with project data.
• Create a useful project output, such as a figure, table, script, notebook, short report, or presentation.
• Share weekly progress, questions, figures, papers, or challenges with the group.
• Explain their results, limitations, and next steps clearly.
• Learn practical data skills while making progress on their own project.

This experience can help students prepare for undergraduate research, paid internships, graduate school, technical careers, and future work in data-driven fields.

• Commit weekly time and make steady progress.
• Meet with your mentor regularly and come prepared with updates + questions.
• Communicate early if you are stuck or your schedule changes.
• Document your work every week:
  1. Weekly progress log
  2. Data/model log (what data you used + what you changed)
  3. Reproducible scripts/notebooks + labeled figures
• Complete the pre- and post-surveys to help evaluate and improve the pathway.
• Finish the required deliverables: short report + short slides + a complete project folder that reproduces your results.

• For Students
• For Mentors

• Expectations Guide 2026 (PDF)
• Participant Guide 2026 (PDF)

• The Student- Mentor Workspace Organization & Structure
• Data Skills Pathway Projects
• Data Skills Pathway Fellows (Students)
• Data Skills Pathway Mentors

• Atmos Home
• DokuWiki Syntax
• UND launches Data Skills Pathway to prepare students for AI-age careers
• David Delene's Home Page
• AtSc 494 - Spring 2026